Leveraging AI-assisted Central Statistical Monitoring to Elevate Clinical Trial Oversight and Data Quality

Music City Center 

With the increasing adoption of Risk-Based Quality Management (RBQM) in clinical trials, Central Statistical Monitoring (CSM) has also gained growing recognition and application. CSM not only enables the early detection of abnormal trends and potential issues at sites but also aids Quality Assurance team in identifying potential audit risk sites. This prepares sponsors for regulatory inspections and ensures more efficient oversight of clinical trial operations and the maintenance of data quality. In the digital age, the application of artificial intelligence has empowered CSM to more efficiently and accurately identify and assess anomalies and inconsistencies in data. It proactively helps to identify errors, fraud, or other issues that may compromise the validity of trial results, thereby enhancing data quality and ensuring trial compliance, patient safety, and data integrity. The digitalization tool will also enhance cross-functional collaboration and communication to facilitate the seamless implementation of Risk-Based Quality Management (RBQM).
This topic will delve into the practical applications of AI-assisted Central Statistical Monitoring within digital platform, exploring how it can be effectively implemented to elevate clinical trial oversight and data quality. We will also touch upon the challenges and opportunities associated with this innovative approach, hoping to offer some valuable insights that can guide its successful integration into the clinical trial landscape.